Enhancing Security Requirements Engineering by Organisational Learning

More and more software projects today are security-related in one way or the other. Requirements engineers often fail to recognise indicators for security problems which is a major source of security problems in practice. Identifying security-relevant requirements is labour-intensive and errorprone. In order to facilitate the security requirements elicitation process, we present an approach supporting organisational learning on security requirements by establishing company-wide experience resources, and a socio-technical network to benefit from them. The approach is based on modelling the flow of requirements and related experiences. Based on those models, we enable people to exchange experiences about security-requirements while they write and discuss project requirements. At the same time, the approach enables participating stakeholders to learn while they write requirements. This can increase security awareness and facilitate learning on both individual and organisational levels. As a basis for our approach, we introduce heuristic assistant tools which support reuse of existing security-related experiences. In particular, they include Bayesian classifiers which issue a warning automatically when new requirements seem to be security-relevant. Our results indicate that this is feasible, in particular if the classifier is trained with domain specific data and documents from previous projects. We show how the ability to identify security-relevant requirements can be improved using this approach. We illustrate our approach by providing a step-by-step example of how we improved the security requirements engineering process at the European Telecommunications Standards Institute (ETSI) and report on experiences made in this application.

Machine learning techniques for music information retrieval

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015

O ensino da multimédia com recurso ao Project Based Learning : realização de vídeos promocionais de um estabelecimento de ensino

Relatório da prática de ensino supervisionada, Mestrado em Ensino de Informática, Universidade de Lisboa, 2014

Reinforcement learning across development in humans

Tese de mestrado, Neurociências, Faculdade de Medicina, Universidade de Lisboa, 2015

Teaching by leading and managing learning environments

The aim of this chapter is to promote an understanding of how different environments or settings within which students are asked or required to learn - such as large groups, small groups and laboratory and practice settings – have an impact on how they approach their learning and hence on the design and delivery of teaching. It provides an overview of underpinning principles and concepts before exploring their application in practice. The focus is on face-to-face teaching and learning.

Network mechanisms of intentional learning

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flex- ible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ven- tral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple dis- tinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.

Using Modern Tools to Explain the Use of the Smith Chart

The concept of explaining the use of an old tool like the Smith chart, using modern tools like MATLAB [1] scripts in combination with e-learning facilities, is exemplified by two MATLAB scripts. These display, step by step, the graphical procedure that must be used to solve the double-stub impedance-matching problem. These two scripts correspond to two different possible ways to analyze this matching problem, and they are important for students to learn by themselves.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

A mobile robot platform for open learning based on serious games and remote laboratories

Within the pedagogical community, Serious Games have arisen as a viable alternative to traditional course-based learning materials. Until now, they have been based strictly on software solutions. Meanwhile, research into Remote Laboratories has shown that they are a viable, low-cost solution for experimentation in an engineering context, providing uninterrupted access, low-maintenance requirements, and a heightened sense of reality when compared to simulations. This paper will propose a solution where both approaches are combined to deliver a Remote Laboratory-based Serious Game for use in engineering and school education. The platform for this system is the WebLab-Deusto Framework, already well-tested within the remote laboratory context, and based on open standards. The laboratory allows users to control a mobile robot in a labyrinth environment and take part in an interactive game where they must locate and correctly answer several questions, the subject of which can be adapted to educators' needs. It also integrates the Google Blockly graphical programming language, allowing students to learn basic programming and logic principles without needing to understand complex syntax.

A criança invisual é tão apta à aprendizagem como as restantes

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção do grau de mestre em Ensino do 1.º e 2.º Ciclo do Ensino Básico

Modelling an e-learning environment for learning programming languages

It is widely accepted that solving programming exercises is fundamental to learn how to program. Nevertheless, solving exercises is only effective if students receive an assessment on their work. An exercise solved wrong will consolidate a false belief, and without feedback many students will not be able to overcome their difficulties. However, creating, managing and accessing a large number of exercises, covering all the points in the curricula of a programming course, in classes with large number of students, can be a daunting task without the appropriated tools working in unison. This involves a diversity of tools, from the environments where programs are coded, to automatic program evaluators providing feedback on the attempts of students, passing through the authoring, management and sequencing of programming exercises as learning objects. We believe that the integration of these tools will have a great impact in acquiring programming skills. Our research objective is to manage and coordinate a network of eLearning systems where students can solve computer programming exercises. Networks of this kind include systems such as learning management systems (LMS), evaluation engines (EE), learning objects repositories (LOR) and exercise resolution environments (ERE). Our strategy to achieve the interoperability among these tools is based on a shared definition of programming exercise as a Learning Object (LO).

Can poor readers visually recognize words they are unable to read? /

The ability to learn new reading vocabulary was assessed in 30 grade 3 poor readers reading approximately one to two years below grade level; the results of the assessment were compared to the performance abilities of 33 normal readers in grade 3 as obtained from an earlier study that employed the same approach and stimuli. The purpose of the study was to examine the strategies employed by poor readers in the acquisition of new reading vocabulary. Students were randomly assigned to either a treatment group (Mixed Phonics Explicit), or to a control group (Phonics Implicit). Subjects in the Mixed Phonics Explicit groups received explicit letter/sound correspondence training. Subjects in the Phonics Implicit group were asked to re-read the presented pseudo-words, receiving corrective feedback when necessary. The stimuli on which the subjects were trained involved a list of six pseudo-words presented in sentences as surnames. The training involved a teaching and test format on each trial for a total of six trials or until criterion had been reached. The results suggested that both normal and poor readers engage in visual learning and verbal coding when acquiring new reading vocabulary. However, poor readers appear to engage in less verbal coding than normal readers. Between group comparisons showed no difference between poor and normal readers in trials and errors to criterion in the visual recognition memory measure. However, normal readers performed significantly better in reading their visual recognition choices.

The use of explicit algorithms and episodic context to teach subtraction to students with learning problems in mathematics /

This research attempted to address the question of the role of explicit algorithms and episodic contexts in the acquisition of computational procedures for regrouping in subtraction. Three groups of students having difficulty learning to subtract with regrouping were taught procedures for doing so through either an explicit algorithm, an episodic content or an examples approach. It was hypothesized that the use of an explicit algorithm represented in a flow chart format would facilitate the acquisition and retention of specific procedural steps relative to the other two conditions. On the other hand, the use of paragraph stories to create episodic content was expected to facilitate the retrieval of algorithms, particularly in a mixed presentation format. The subjects were tested on similar, near, and far transfer questions over a four-day period. Near and far transfer algorithms were also introduced on Day Two. The results suggested that both explicit and episodic context facilitate performance on questions requiring subtraction with regrouping. However, the differential effects of these two approaches on near and far transfer questions were not as easy to identify. Explicit algorithms may facilitate the acquisition of specific procedural steps while at the same time inhibiting the application of such steps to transfer questions. Similarly, the value of episodic context in cuing the retrieval of an algorithm may be limited by the ability of a subject to identify and classify a new question as an exemplar of a particular episodically deflned problem type or category. The implications of these findings in relation to the procedures employed in the teaching of Mathematics to students with learning problems are discussed in detail.

Learning English as an adult: a narrative of women's improvised and empowered lives /

This thesis is a narrative inquiry of learning English as an adult. It stories the journey of 7 women, including me, and unravels lived experiences that serve as learning models. Learning English as an adult presents challenges and results in lifelong implications both in personal and professional life. Every learner's experience is imique and, when reflected upon, each experience is a valuable source of knowledge for constructing meanings and forging new identities. The stories are testimony to the participants' lives: interrupted yet improvised, silenced yet roused, dependent yet independent, intimidated yet courageous, vulnerable yet empowered. The personal experiences elucidate the passion, the inner voices, the dreams, and the rewards that compel persistence in learning a new language and releaming new social roles. The stories provide encouragement and hope to other women who are learning or will learn English in their adult years, and the lived experiences will offer insights for English language teachers. This thesis employs the phenomenology methodology of research with heuristic (discovery) and hermeneutical (interpretative) approaches using the reflective-responsivereflexive writing and interviewing methods for data gathering and unravelling. The narrative inquiry approach reaffirms that storytelling is an important tool in conducting research and constructing new knowledge. This thesis narrates a new story about sharing experiences, interconnecting, and continuing to learn.